Overview

Dataset statistics

Number of variables46
Number of observations602
Missing cells4461
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory237.2 KiB
Average record size in memory403.5 B

Variable types

Text21
Categorical7
DateTime8
Numeric8
Unsupported2

Alerts

degreeCategory2 is highly overall correlated with fieldCategory2High correlation
endorsement1 is highly overall correlated with endorsement2 and 3 other fieldsHigh correlation
endorsement2 is highly overall correlated with endorsement1 and 4 other fieldsHigh correlation
endorsement3 is highly overall correlated with endorsement1 and 4 other fieldsHigh correlation
endorsement4 is highly overall correlated with endorsement1 and 4 other fieldsHigh correlation
endorsement5 is highly overall correlated with endorsement1 and 4 other fieldsHigh correlation
endorsement6 is highly overall correlated with endorsement2 and 3 other fieldsHigh correlation
fieldCategory2 is highly overall correlated with degreeCategory2High correlation
jobTenureCategory is highly overall correlated with jobTenureYearsHigh correlation
jobTenureYears is highly overall correlated with jobTenureCategoryHigh correlation
company is highly imbalanced (68.8%)Imbalance
jobLocation has 137 (22.8%) missing valuesMissing
company2 has 19 (3.2%) missing valuesMissing
jobTitle2 has 20 (3.3%) missing valuesMissing
jobDateRange2 has 20 (3.3%) missing valuesMissing
jobStartedSince2 has 21 (3.5%) missing valuesMissing
school has 30 (5.0%) missing valuesMissing
schoolUrl has 101 (16.8%) missing valuesMissing
schoolDateRange has 97 (16.1%) missing valuesMissing
schoolUrl2 has 297 (49.3%) missing valuesMissing
schoolDegree2 has 243 (40.4%) missing valuesMissing
schoolDateRange2 has 232 (38.5%) missing valuesMissing
allSkills has 51 (8.5%) missing valuesMissing
skill1 has 51 (8.5%) missing valuesMissing
endorsement1 has 241 (40.0%) missing valuesMissing
skill2 has 52 (8.6%) missing valuesMissing
endorsement2 has 238 (39.5%) missing valuesMissing
skill3 has 53 (8.8%) missing valuesMissing
endorsement3 has 223 (37.0%) missing valuesMissing
skill4 has 57 (9.5%) missing valuesMissing
endorsement4 has 241 (40.0%) missing valuesMissing
skill5 has 64 (10.6%) missing valuesMissing
endorsement5 has 246 (40.9%) missing valuesMissing
skill6 has 81 (13.5%) missing valuesMissing
endorsement6 has 256 (42.5%) missing valuesMissing
error has 602 (100.0%) missing valuesMissing
schoolStartDate has 97 (16.1%) missing valuesMissing
schoolEndDate has 97 (16.1%) missing valuesMissing
schoolStartDate2 has 232 (38.5%) missing valuesMissing
schoolEndDate2 has 232 (38.5%) missing valuesMissing
totalExperienceYears has 93 (15.4%) missing valuesMissing
fullName has unique valuesUnique
error is an unsupported type, check if it needs cleaning or further analysisUnsupported
weighted_skills is an unsupported type, check if it needs cleaning or further analysisUnsupported
totalExperienceYears has 20 (3.3%) zerosZeros

Reproduction

Analysis started2024-06-24 13:34:46.583494
Analysis finished2024-06-24 13:35:15.349402
Duration28.77 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct123
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:15.599778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length49
Median length46
Mean length28.898671
Min length6

Characters and Unicode

Total characters17397
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)15.1%

Sample

1st rowGreater Melbourne Area
2nd rowMelbourne, Victoria
3rd rowGreater Sydney Area
4th rowMelbourne, Victoria, Australia
5th rowDelhi, India
ValueCountFrequency (%)
australia 345
15.9%
area 191
 
8.8%
greater 190
 
8.7%
sydney 156
 
7.2%
melbourne 145
 
6.7%
new 113
 
5.2%
south 113
 
5.2%
wales 108
 
5.0%
victoria 98
 
4.5%
australian 74
 
3.4%
Other values (162) 643
29.5%
2024-06-24T13:35:16.361927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2037
 
11.7%
r 1782
 
10.2%
e 1651
 
9.5%
1574
 
9.0%
t 1132
 
6.5%
i 983
 
5.7%
l 865
 
5.0%
n 774
 
4.4%
u 739
 
4.2%
, 725
 
4.2%
Other values (47) 5135
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2037
 
11.7%
r 1782
 
10.2%
e 1651
 
9.5%
1574
 
9.0%
t 1132
 
6.5%
i 983
 
5.7%
l 865
 
5.0%
n 774
 
4.4%
u 739
 
4.2%
, 725
 
4.2%
Other values (47) 5135
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2037
 
11.7%
r 1782
 
10.2%
e 1651
 
9.5%
1574
 
9.0%
t 1132
 
6.5%
i 983
 
5.7%
l 865
 
5.0%
n 774
 
4.4%
u 739
 
4.2%
, 725
 
4.2%
Other values (47) 5135
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2037
 
11.7%
r 1782
 
10.2%
e 1651
 
9.5%
1574
 
9.0%
t 1132
 
6.5%
i 983
 
5.7%
l 865
 
5.0%
n 774
 
4.4%
u 739
 
4.2%
, 725
 
4.2%
Other values (47) 5135
29.5%

fullName
Text

UNIQUE 

Distinct602
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:16.899202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length51
Median length23
Mean length13.028239
Min length7

Characters and Unicode

Total characters7843
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique602 ?
Unique (%)100.0%

Sample

1st rowWill Adams
2nd rowAmmrith Adithya
3rd rowPrabin Agarwal
4th rowPriyanka Agarwal
5th rowAkansha Aggarwal
ValueCountFrequency (%)
david 14
 
1.1%
nguyen 7
 
0.6%
andrew 7
 
0.6%
paul 7
 
0.6%
john 6
 
0.5%
gupta 6
 
0.5%
michael 6
 
0.5%
kumar 6
 
0.5%
sharma 6
 
0.5%
li 5
 
0.4%
Other values (983) 1188
94.4%
2024-06-24T13:35:17.724546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 921
 
11.7%
663
 
8.5%
n 588
 
7.5%
e 561
 
7.2%
i 506
 
6.5%
r 401
 
5.1%
o 358
 
4.6%
h 340
 
4.3%
l 315
 
4.0%
s 230
 
2.9%
Other values (54) 2960
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 921
 
11.7%
663
 
8.5%
n 588
 
7.5%
e 561
 
7.2%
i 506
 
6.5%
r 401
 
5.1%
o 358
 
4.6%
h 340
 
4.3%
l 315
 
4.0%
s 230
 
2.9%
Other values (54) 2960
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 921
 
11.7%
663
 
8.5%
n 588
 
7.5%
e 561
 
7.2%
i 506
 
6.5%
r 401
 
5.1%
o 358
 
4.6%
h 340
 
4.3%
l 315
 
4.0%
s 230
 
2.9%
Other values (54) 2960
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 921
 
11.7%
663
 
8.5%
n 588
 
7.5%
e 561
 
7.2%
i 506
 
6.5%
r 401
 
5.1%
o 358
 
4.6%
h 340
 
4.3%
l 315
 
4.0%
s 230
 
2.9%
Other values (54) 2960
37.7%

company
Categorical

IMBALANCE 

Distinct16
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
KPMG Australia
479 
KPMG
62 
KPMG US
 
15
KPMG UK
 
14
KPMG India
 
11
Other values (11)
 
21

Length

Max length31
Median length14
Mean length12.616279
Min length4

Characters and Unicode

Total characters7595
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.8%

Sample

1st rowKPMG Australia
2nd rowKPMG
3rd rowKPMG Australia
4th rowKPMG Australia
5th rowKPMG

Common Values

ValueCountFrequency (%)
KPMG Australia 479
79.6%
KPMG 62
 
10.3%
KPMG US 15
 
2.5%
KPMG UK 14
 
2.3%
KPMG India 11
 
1.8%
KPMG Canada 4
 
0.7%
KPMG Vietnam 3
 
0.5%
KPMG China 3
 
0.5%
KPMG Nederland 2
 
0.3%
KPMG Global Services (KGS) 2
 
0.3%
Other values (6) 7
 
1.2%

Length

2024-06-24T13:35:18.054943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kpmg 602
52.2%
australia 479
41.5%
us 15
 
1.3%
uk 14
 
1.2%
india 11
 
1.0%
canada 4
 
0.3%
systems 3
 
0.3%
hands-on 3
 
0.3%
services 3
 
0.3%
china 3
 
0.3%
Other values (11) 16
 
1.4%

Most occurring characters

ValueCountFrequency (%)
a 1000
13.2%
K 618
 
8.1%
G 606
 
8.0%
P 603
 
7.9%
M 602
 
7.9%
551
 
7.3%
i 500
 
6.6%
s 493
 
6.5%
l 488
 
6.4%
t 487
 
6.4%
Other values (29) 1647
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1000
13.2%
K 618
 
8.1%
G 606
 
8.0%
P 603
 
7.9%
M 602
 
7.9%
551
 
7.3%
i 500
 
6.6%
s 493
 
6.5%
l 488
 
6.4%
t 487
 
6.4%
Other values (29) 1647
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1000
13.2%
K 618
 
8.1%
G 606
 
8.0%
P 603
 
7.9%
M 602
 
7.9%
551
 
7.3%
i 500
 
6.6%
s 493
 
6.5%
l 488
 
6.4%
t 487
 
6.4%
Other values (29) 1647
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1000
13.2%
K 618
 
8.1%
G 606
 
8.0%
P 603
 
7.9%
M 602
 
7.9%
551
 
7.3%
i 500
 
6.6%
s 493
 
6.5%
l 488
 
6.4%
t 487
 
6.4%
Other values (29) 1647
21.7%

jobTitle
Categorical

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
Associate Director
165 
Manager
124 
Senior Consultant
108 
Director
93 
Consultant
87 

Length

Max length18
Median length17
Mean length12.39701
Min length7

Characters and Unicode

Total characters7463
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSenior Consultant
2nd rowConsultant
3rd rowAssociate Director
4th rowAssociate Director
5th rowManager

Common Values

ValueCountFrequency (%)
Associate Director 165
27.4%
Manager 124
20.6%
Senior Consultant 108
17.9%
Director 93
15.4%
Consultant 87
14.5%
Partner 25
 
4.2%

Length

2024-06-24T13:35:18.322743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T13:35:18.619651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
director 258
29.5%
consultant 195
22.3%
associate 165
18.9%
manager 124
14.2%
senior 108
12.3%
partner 25
 
2.9%

Most occurring characters

ValueCountFrequency (%)
t 838
11.2%
r 798
10.7%
o 726
9.7%
e 680
9.1%
n 647
8.7%
a 633
8.5%
i 531
 
7.1%
s 525
 
7.0%
c 423
 
5.7%
273
 
3.7%
Other values (9) 1389
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 838
11.2%
r 798
10.7%
o 726
9.7%
e 680
9.1%
n 647
8.7%
a 633
8.5%
i 531
 
7.1%
s 525
 
7.0%
c 423
 
5.7%
273
 
3.7%
Other values (9) 1389
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 838
11.2%
r 798
10.7%
o 726
9.7%
e 680
9.1%
n 647
8.7%
a 633
8.5%
i 531
 
7.1%
s 525
 
7.0%
c 423
 
5.7%
273
 
3.7%
Other values (9) 1389
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 838
11.2%
r 798
10.7%
o 726
9.7%
e 680
9.1%
n 647
8.7%
a 633
8.5%
i 531
 
7.1%
s 525
 
7.0%
c 423
 
5.7%
273
 
3.7%
Other values (9) 1389
18.6%

jobLocation
Text

MISSING 

Distinct78
Distinct (%)16.8%
Missing137
Missing (%)22.8%
Memory size25.6 KiB
2024-06-24T13:35:19.004555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length59
Median length47
Mean length30.421505
Min length5

Characters and Unicode

Total characters14146
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)8.6%

Sample

1st rowMelbourne, Victoria, Australia
2nd rowMelbourne, Victoria, Australia
3rd rowSydney, New South Wales, Australia
4th rowMelbourne, Victoria, Australia
5th rowDelhi, India
ValueCountFrequency (%)
australia 402
23.0%
sydney 135
 
7.7%
south 123
 
7.0%
new 118
 
6.8%
melbourne 117
 
6.7%
wales 115
 
6.6%
victoria 103
 
5.9%
· 64
 
3.7%
canberra 63
 
3.6%
australian 56
 
3.2%
Other values (98) 451
25.8%
2024-06-24T13:35:19.718767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1584
 
11.2%
1282
 
9.1%
r 1135
 
8.0%
i 974
 
6.9%
e 941
 
6.7%
t 915
 
6.5%
l 814
 
5.8%
u 749
 
5.3%
, 727
 
5.1%
s 673
 
4.8%
Other values (43) 4352
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1584
 
11.2%
1282
 
9.1%
r 1135
 
8.0%
i 974
 
6.9%
e 941
 
6.7%
t 915
 
6.5%
l 814
 
5.8%
u 749
 
5.3%
, 727
 
5.1%
s 673
 
4.8%
Other values (43) 4352
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1584
 
11.2%
1282
 
9.1%
r 1135
 
8.0%
i 974
 
6.9%
e 941
 
6.7%
t 915
 
6.5%
l 814
 
5.8%
u 749
 
5.3%
, 727
 
5.1%
s 673
 
4.8%
Other values (43) 4352
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1584
 
11.2%
1282
 
9.1%
r 1135
 
8.0%
i 974
 
6.9%
e 941
 
6.7%
t 915
 
6.5%
l 814
 
5.8%
u 749
 
5.3%
, 727
 
5.1%
s 673
 
4.8%
Other values (43) 4352
30.8%
Distinct123
Distinct (%)20.6%
Missing5
Missing (%)0.8%
Memory size25.6 KiB
2024-06-24T13:35:20.095972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length27
Median length18
Mean length18.008375
Min length11

Characters and Unicode

Total characters10751
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)11.7%

Sample

1st rowOct 2020 - Feb 2022
2nd rowFeb 2024 - Present
3rd rowMar 2022 - Present
4th rowMay 2022 - Present
5th rowSep 2023 - Present
ValueCountFrequency (%)
597
24.8%
present 568
23.5%
2022 183
 
7.6%
2023 175
 
7.3%
jul 119
 
4.9%
2021 109
 
4.5%
oct 106
 
4.4%
jan 97
 
4.0%
feb 89
 
3.7%
2024 65
 
2.7%
Other values (28) 304
12.6%
2024-06-24T13:35:20.741171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1815
16.9%
2 1360
12.6%
e 1275
11.9%
n 691
 
6.4%
t 674
 
6.3%
0 651
 
6.1%
r 621
 
5.8%
- 597
 
5.6%
s 569
 
5.3%
P 568
 
5.3%
Other values (28) 1930
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1815
16.9%
2 1360
12.6%
e 1275
11.9%
n 691
 
6.4%
t 674
 
6.3%
0 651
 
6.1%
r 621
 
5.8%
- 597
 
5.6%
s 569
 
5.3%
P 568
 
5.3%
Other values (28) 1930
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1815
16.9%
2 1360
12.6%
e 1275
11.9%
n 691
 
6.4%
t 674
 
6.3%
0 651
 
6.1%
r 621
 
5.8%
- 597
 
5.6%
s 569
 
5.3%
P 568
 
5.3%
Other values (28) 1930
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1815
16.9%
2 1360
12.6%
e 1275
11.9%
n 691
 
6.4%
t 674
 
6.3%
0 651
 
6.1%
r 621
 
5.8%
- 597
 
5.6%
s 569
 
5.3%
P 568
 
5.3%
Other values (28) 1930
18.0%
Distinct95
Distinct (%)15.9%
Missing6
Missing (%)1.0%
Memory size25.6 KiB
Minimum1996-11-01 00:00:00
Maximum2024-05-01 00:00:00
2024-06-24T13:35:21.084365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:21.367088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct95
Distinct (%)15.9%
Missing6
Missing (%)1.0%
Memory size25.6 KiB
2024-06-24T13:35:21.649456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.6073826
Min length4

Characters and Unicode

Total characters5130
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)6.7%

Sample

1st row1 yr 5 mos
2nd row5 mos
3rd row2 yrs 4 mos
4th row2 yrs 2 mos
5th row10 mos
ValueCountFrequency (%)
mos 454
23.2%
yrs 304
15.5%
1 221
11.3%
2 211
10.8%
yr 196
10.0%
6 107
 
5.5%
5 99
 
5.1%
9 97
 
5.0%
3 80
 
4.1%
4 36
 
1.8%
Other values (14) 153
 
7.8%
2024-06-24T13:35:22.237433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1362
26.5%
s 758
14.8%
y 500
 
9.7%
r 500
 
9.7%
m 479
 
9.3%
o 479
 
9.3%
1 319
 
6.2%
2 217
 
4.2%
6 109
 
2.1%
5 101
 
2.0%
Other values (6) 306
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1362
26.5%
s 758
14.8%
y 500
 
9.7%
r 500
 
9.7%
m 479
 
9.3%
o 479
 
9.3%
1 319
 
6.2%
2 217
 
4.2%
6 109
 
2.1%
5 101
 
2.0%
Other values (6) 306
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1362
26.5%
s 758
14.8%
y 500
 
9.7%
r 500
 
9.7%
m 479
 
9.3%
o 479
 
9.3%
1 319
 
6.2%
2 217
 
4.2%
6 109
 
2.1%
5 101
 
2.0%
Other values (6) 306
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1362
26.5%
s 758
14.8%
y 500
 
9.7%
r 500
 
9.7%
m 479
 
9.3%
o 479
 
9.3%
1 319
 
6.2%
2 217
 
4.2%
6 109
 
2.1%
5 101
 
2.0%
Other values (6) 306
 
6.0%

company2
Text

MISSING 

Distinct242
Distinct (%)41.5%
Missing19
Missing (%)3.2%
Memory size25.6 KiB
2024-06-24T13:35:22.781765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length63
Median length52
Mean length14.379074
Min length2

Characters and Unicode

Total characters8383
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)35.7%

Sample

1st rowKPMG Australia
2nd rowCommonwealth Bank
3rd rowAccenture Australia
4th rowDXC Technology
5th rowKPMG
ValueCountFrequency (%)
kpmg 298
24.1%
australia 268
21.6%
pwc 20
 
1.6%
14
 
1.1%
group 13
 
1.1%
systems 13
 
1.1%
ltd 11
 
0.9%
deloitte 11
 
0.9%
india 10
 
0.8%
accenture 10
 
0.8%
Other values (390) 570
46.0%
2024-06-24T13:35:23.736920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 800
 
9.5%
655
 
7.8%
i 544
 
6.5%
t 520
 
6.2%
r 486
 
5.8%
s 471
 
5.6%
l 424
 
5.1%
u 383
 
4.6%
e 370
 
4.4%
P 363
 
4.3%
Other values (72) 3367
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 800
 
9.5%
655
 
7.8%
i 544
 
6.5%
t 520
 
6.2%
r 486
 
5.8%
s 471
 
5.6%
l 424
 
5.1%
u 383
 
4.6%
e 370
 
4.4%
P 363
 
4.3%
Other values (72) 3367
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 800
 
9.5%
655
 
7.8%
i 544
 
6.5%
t 520
 
6.2%
r 486
 
5.8%
s 471
 
5.6%
l 424
 
5.1%
u 383
 
4.6%
e 370
 
4.4%
P 363
 
4.3%
Other values (72) 3367
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 800
 
9.5%
655
 
7.8%
i 544
 
6.5%
t 520
 
6.2%
r 486
 
5.8%
s 471
 
5.6%
l 424
 
5.1%
u 383
 
4.6%
e 370
 
4.4%
P 363
 
4.3%
Other values (72) 3367
40.2%

jobTitle2
Text

MISSING 

Distinct388
Distinct (%)66.7%
Missing20
Missing (%)3.3%
Memory size25.6 KiB
2024-06-24T13:35:24.185512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length87
Median length69
Mean length24.35567
Min length5

Characters and Unicode

Total characters14175
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)59.5%

Sample

1st rowConsultant
2nd rowEarly Customer Engagement Specialist (Financial Assist Solutions)
3rd rowManager
4th rowSenior Technical Consultant
5th rowExecutive
ValueCountFrequency (%)
148
 
8.1%
manager 141
 
7.7%
consultant 138
 
7.5%
senior 100
 
5.5%
director 93
 
5.1%
associate 59
 
3.2%
technology 45
 
2.5%
lead 42
 
2.3%
data 37
 
2.0%
advisory 36
 
2.0%
Other values (341) 994
54.2%
2024-06-24T13:35:24.954801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1264
 
8.9%
n 1228
 
8.7%
e 1226
 
8.6%
a 1145
 
8.1%
t 1066
 
7.5%
r 945
 
6.7%
o 905
 
6.4%
i 847
 
6.0%
s 709
 
5.0%
c 538
 
3.8%
Other values (56) 4302
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1264
 
8.9%
n 1228
 
8.7%
e 1226
 
8.6%
a 1145
 
8.1%
t 1066
 
7.5%
r 945
 
6.7%
o 905
 
6.4%
i 847
 
6.0%
s 709
 
5.0%
c 538
 
3.8%
Other values (56) 4302
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1264
 
8.9%
n 1228
 
8.7%
e 1226
 
8.6%
a 1145
 
8.1%
t 1066
 
7.5%
r 945
 
6.7%
o 905
 
6.4%
i 847
 
6.0%
s 709
 
5.0%
c 538
 
3.8%
Other values (56) 4302
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1264
 
8.9%
n 1228
 
8.7%
e 1226
 
8.6%
a 1145
 
8.1%
t 1066
 
7.5%
r 945
 
6.7%
o 905
 
6.4%
i 847
 
6.0%
s 709
 
5.0%
c 538
 
3.8%
Other values (56) 4302
30.3%

jobDateRange2
Text

MISSING 

Distinct473
Distinct (%)81.3%
Missing20
Missing (%)3.3%
Memory size25.6 KiB
2024-06-24T13:35:25.463854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length19
Mean length18.785223
Min length8

Characters and Unicode

Total characters10933
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique402 ?
Unique (%)69.1%

Sample

1st rowSep 2018 - Oct 2020
2nd rowMar 2020 - Aug 2020
3rd rowOct 2016 - Feb 2022
4th rowSep 2021 - May 2022
5th rowSep 2022 - Sep 2023
ValueCountFrequency (%)
581
20.5%
2022 283
 
10.0%
2021 251
 
8.9%
oct 164
 
5.8%
feb 155
 
5.5%
2023 155
 
5.5%
jul 118
 
4.2%
sep 115
 
4.1%
jan 110
 
3.9%
jun 96
 
3.4%
Other values (32) 801
28.3%
2024-06-24T13:35:26.458235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2247
20.6%
2 2201
20.1%
0 1225
11.2%
- 581
 
5.3%
1 514
 
4.7%
e 471
 
4.3%
J 324
 
3.0%
u 276
 
2.5%
n 273
 
2.5%
c 231
 
2.1%
Other values (28) 2590
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2247
20.6%
2 2201
20.1%
0 1225
11.2%
- 581
 
5.3%
1 514
 
4.7%
e 471
 
4.3%
J 324
 
3.0%
u 276
 
2.5%
n 273
 
2.5%
c 231
 
2.1%
Other values (28) 2590
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2247
20.6%
2 2201
20.1%
0 1225
11.2%
- 581
 
5.3%
1 514
 
4.7%
e 471
 
4.3%
J 324
 
3.0%
u 276
 
2.5%
n 273
 
2.5%
c 231
 
2.1%
Other values (28) 2590
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2247
20.6%
2 2201
20.1%
0 1225
11.2%
- 581
 
5.3%
1 514
 
4.7%
e 471
 
4.3%
J 324
 
3.0%
u 276
 
2.5%
n 273
 
2.5%
c 231
 
2.1%
Other values (28) 2590
23.7%

jobStartedSince2
Date

MISSING 

Distinct128
Distinct (%)22.0%
Missing21
Missing (%)3.5%
Memory size25.6 KiB
Minimum1994-04-01 00:00:00
Maximum2024-02-01 00:00:00
2024-06-24T13:35:27.000423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:27.525246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

school
Text

MISSING 

Distinct248
Distinct (%)43.4%
Missing30
Missing (%)5.0%
Memory size25.6 KiB
2024-06-24T13:35:28.155909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length127
Median length58
Mean length25.26049
Min length3

Characters and Unicode

Total characters14449
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)35.0%

Sample

1st rowMonash University
2nd rowMonash University
3rd rowInstitute of Chartered Accountant of India
4th rowCollege Of Engineering Roorkee
5th rowThe Institute of Chartered Accountants of India
ValueCountFrequency (%)
university 372
19.3%
of 258
 
13.4%
technology 70
 
3.6%
the 68
 
3.5%
unsw 56
 
2.9%
sydney 47
 
2.4%
school 47
 
2.4%
monash 39
 
2.0%
institute 37
 
1.9%
queensland 34
 
1.8%
Other values (399) 901
46.7%
2024-06-24T13:35:29.435521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1357
 
9.4%
i 1205
 
8.3%
e 1199
 
8.3%
n 1154
 
8.0%
o 800
 
5.5%
t 785
 
5.4%
s 752
 
5.2%
r 748
 
5.2%
a 741
 
5.1%
y 570
 
3.9%
Other values (62) 5138
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1357
 
9.4%
i 1205
 
8.3%
e 1199
 
8.3%
n 1154
 
8.0%
o 800
 
5.5%
t 785
 
5.4%
s 752
 
5.2%
r 748
 
5.2%
a 741
 
5.1%
y 570
 
3.9%
Other values (62) 5138
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1357
 
9.4%
i 1205
 
8.3%
e 1199
 
8.3%
n 1154
 
8.0%
o 800
 
5.5%
t 785
 
5.4%
s 752
 
5.2%
r 748
 
5.2%
a 741
 
5.1%
y 570
 
3.9%
Other values (62) 5138
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1357
 
9.4%
i 1205
 
8.3%
e 1199
 
8.3%
n 1154
 
8.0%
o 800
 
5.5%
t 785
 
5.4%
s 752
 
5.2%
r 748
 
5.2%
a 741
 
5.1%
y 570
 
3.9%
Other values (62) 5138
35.6%

schoolUrl
Text

MISSING 

Distinct193
Distinct (%)38.5%
Missing101
Missing (%)16.8%
Memory size25.6 KiB
2024-06-24T13:35:30.014232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length42
Median length41
Mean length39.307385
Min length38

Characters and Unicode

Total characters19693
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)29.7%

Sample

1st rowhttps://www.linkedin.com/company/5663/
2nd rowhttps://www.linkedin.com/company/5663/
3rd rowhttps://www.linkedin.com/company/1968486/
4th rowhttps://www.linkedin.com/company/739903/
5th rowhttps://www.linkedin.com/company/363736/
ValueCountFrequency (%)
https://www.linkedin.com/company/6096 45
 
9.0%
https://www.linkedin.com/company/5663 38
 
7.6%
https://www.linkedin.com/company/166678 26
 
5.2%
https://www.linkedin.com/company/162587 25
 
5.0%
https://www.linkedin.com/company/5885 21
 
4.2%
https://www.linkedin.com/company/5677 18
 
3.6%
https://www.linkedin.com/company/165624 18
 
3.6%
https://www.linkedin.com/company/166676 15
 
3.0%
https://www.linkedin.com/company/166664 13
 
2.6%
https://www.linkedin.com/company/7763 12
 
2.4%
Other values (183) 270
53.9%
2024-06-24T13:35:30.763638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2505
 
12.7%
w 1503
 
7.6%
n 1503
 
7.6%
c 1002
 
5.1%
t 1002
 
5.1%
p 1002
 
5.1%
. 1002
 
5.1%
i 1002
 
5.1%
m 1002
 
5.1%
o 1002
 
5.1%
Other values (19) 7168
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 2505
 
12.7%
w 1503
 
7.6%
n 1503
 
7.6%
c 1002
 
5.1%
t 1002
 
5.1%
p 1002
 
5.1%
. 1002
 
5.1%
i 1002
 
5.1%
m 1002
 
5.1%
o 1002
 
5.1%
Other values (19) 7168
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 2505
 
12.7%
w 1503
 
7.6%
n 1503
 
7.6%
c 1002
 
5.1%
t 1002
 
5.1%
p 1002
 
5.1%
. 1002
 
5.1%
i 1002
 
5.1%
m 1002
 
5.1%
o 1002
 
5.1%
Other values (19) 7168
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 2505
 
12.7%
w 1503
 
7.6%
n 1503
 
7.6%
c 1002
 
5.1%
t 1002
 
5.1%
p 1002
 
5.1%
. 1002
 
5.1%
i 1002
 
5.1%
m 1002
 
5.1%
o 1002
 
5.1%
Other values (19) 7168
36.4%

schoolDateRange
Text

MISSING 

Distinct234
Distinct (%)46.3%
Missing97
Missing (%)16.1%
Memory size25.6 KiB
2024-06-24T13:35:31.157202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.726733
Min length4

Characters and Unicode

Total characters5922
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)27.5%

Sample

1st row2012 - 2016
2nd row2019 - 2023
3rd row1999 - 2004
4th row2002 - 2006
5th row2012 - 2018
ValueCountFrequency (%)
486
30.3%
2021 68
 
4.2%
2020 63
 
3.9%
2018 62
 
3.9%
2019 60
 
3.7%
2017 54
 
3.4%
2015 48
 
3.0%
2022 45
 
2.8%
2012 45
 
2.8%
2016 44
 
2.7%
Other values (54) 627
39.1%
2024-06-24T13:35:31.854239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1210
20.4%
0 1200
20.3%
1097
18.5%
1 663
11.2%
- 486
8.2%
9 332
 
5.6%
8 131
 
2.2%
7 107
 
1.8%
6 85
 
1.4%
4 85
 
1.4%
Other values (24) 526
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1210
20.4%
0 1200
20.3%
1097
18.5%
1 663
11.2%
- 486
8.2%
9 332
 
5.6%
8 131
 
2.2%
7 107
 
1.8%
6 85
 
1.4%
4 85
 
1.4%
Other values (24) 526
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1210
20.4%
0 1200
20.3%
1097
18.5%
1 663
11.2%
- 486
8.2%
9 332
 
5.6%
8 131
 
2.2%
7 107
 
1.8%
6 85
 
1.4%
4 85
 
1.4%
Other values (24) 526
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1210
20.4%
0 1200
20.3%
1097
18.5%
1 663
11.2%
- 486
8.2%
9 332
 
5.6%
8 131
 
2.2%
7 107
 
1.8%
6 85
 
1.4%
4 85
 
1.4%
Other values (24) 526
8.9%

schoolUrl2
Text

MISSING 

Distinct192
Distinct (%)63.0%
Missing297
Missing (%)49.3%
Memory size25.6 KiB
2024-06-24T13:35:32.317900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length43
Median length42
Mean length39.783607
Min length38

Characters and Unicode

Total characters12134
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)52.5%

Sample

1st rowhttps://www.linkedin.com/company/5663/
2nd rowhttps://www.linkedin.com/company/633270/
3rd rowhttps://www.linkedin.com/company/9584148/
4th rowhttps://www.linkedin.com/company/15109269/
5th rowhttps://www.linkedin.com/company/5663/
ValueCountFrequency (%)
https://www.linkedin.com/company/6096 12
 
3.9%
https://www.linkedin.com/company/5885 10
 
3.3%
https://www.linkedin.com/company/162587 10
 
3.3%
https://www.linkedin.com/company/165624 10
 
3.3%
https://www.linkedin.com/company/5663 10
 
3.3%
https://www.linkedin.com/company/166676 8
 
2.6%
https://www.linkedin.com/company/5677 7
 
2.3%
https://www.linkedin.com/company/19034 7
 
2.3%
https://www.linkedin.com/company/7763 5
 
1.6%
https://www.linkedin.com/company/166678 5
 
1.6%
Other values (182) 221
72.5%
2024-06-24T13:35:33.073190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1525
 
12.6%
w 915
 
7.5%
n 915
 
7.5%
c 610
 
5.0%
p 610
 
5.0%
. 610
 
5.0%
m 610
 
5.0%
i 610
 
5.0%
o 610
 
5.0%
t 610
 
5.0%
Other values (19) 4509
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 1525
 
12.6%
w 915
 
7.5%
n 915
 
7.5%
c 610
 
5.0%
p 610
 
5.0%
. 610
 
5.0%
m 610
 
5.0%
i 610
 
5.0%
o 610
 
5.0%
t 610
 
5.0%
Other values (19) 4509
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 1525
 
12.6%
w 915
 
7.5%
n 915
 
7.5%
c 610
 
5.0%
p 610
 
5.0%
. 610
 
5.0%
m 610
 
5.0%
i 610
 
5.0%
o 610
 
5.0%
t 610
 
5.0%
Other values (19) 4509
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 1525
 
12.6%
w 915
 
7.5%
n 915
 
7.5%
c 610
 
5.0%
p 610
 
5.0%
. 610
 
5.0%
m 610
 
5.0%
i 610
 
5.0%
o 610
 
5.0%
t 610
 
5.0%
Other values (19) 4509
37.2%

schoolDegree2
Text

MISSING 

Distinct345
Distinct (%)96.1%
Missing243
Missing (%)40.4%
Memory size25.6 KiB
2024-06-24T13:35:33.575807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length112
Median length72
Mean length42.420613
Min length3

Characters and Unicode

Total characters15229
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique336 ?
Unique (%)93.6%

Sample

1st rowBachelor's degree, Business (Marketing) (International Business)
2nd rowVCE
3rd rowBachelor of Commerce (B.Com.), Accountancy
4th rowSSC, PCM
5th rowBCA, Computer Applications
ValueCountFrequency (%)
of 152
 
8.0%
bachelor 108
 
5.7%
71
 
3.7%
and 71
 
3.7%
business 70
 
3.7%
science 55
 
2.9%
engineering 48
 
2.5%
management 48
 
2.5%
information 47
 
2.5%
degree 47
 
2.5%
Other values (393) 1177
62.1%
2024-06-24T13:35:34.427222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1541
 
10.1%
e 1381
 
9.1%
n 1189
 
7.8%
o 1042
 
6.8%
a 974
 
6.4%
i 966
 
6.3%
c 774
 
5.1%
r 769
 
5.0%
t 724
 
4.8%
s 640
 
4.2%
Other values (61) 5229
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1541
 
10.1%
e 1381
 
9.1%
n 1189
 
7.8%
o 1042
 
6.8%
a 974
 
6.4%
i 966
 
6.3%
c 774
 
5.1%
r 769
 
5.0%
t 724
 
4.8%
s 640
 
4.2%
Other values (61) 5229
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1541
 
10.1%
e 1381
 
9.1%
n 1189
 
7.8%
o 1042
 
6.8%
a 974
 
6.4%
i 966
 
6.3%
c 774
 
5.1%
r 769
 
5.0%
t 724
 
4.8%
s 640
 
4.2%
Other values (61) 5229
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1541
 
10.1%
e 1381
 
9.1%
n 1189
 
7.8%
o 1042
 
6.8%
a 974
 
6.4%
i 966
 
6.3%
c 774
 
5.1%
r 769
 
5.0%
t 724
 
4.8%
s 640
 
4.2%
Other values (61) 5229
34.3%

schoolDateRange2
Text

MISSING 

Distinct199
Distinct (%)53.8%
Missing232
Missing (%)38.5%
Memory size25.6 KiB
2024-06-24T13:35:34.815863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.321622
Min length4

Characters and Unicode

Total characters4189
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)31.1%

Sample

1st row2012 - 2016
2nd row2015 - 2018
3rd row1999 - 2002
4th row1999 - 2002
5th row2001 - 2004
ValueCountFrequency (%)
351
30.9%
2014 56
 
4.9%
2016 53
 
4.7%
2018 47
 
4.1%
2012 44
 
3.9%
2013 40
 
3.5%
2015 38
 
3.3%
2019 31
 
2.7%
2011 29
 
2.6%
2008 29
 
2.6%
Other values (52) 417
36.7%
2024-06-24T13:35:35.503623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 883
21.1%
2 766
18.3%
765
18.3%
1 546
13.0%
- 351
 
8.4%
9 223
 
5.3%
8 108
 
2.6%
4 87
 
2.1%
6 78
 
1.9%
5 67
 
1.6%
Other values (24) 315
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 883
21.1%
2 766
18.3%
765
18.3%
1 546
13.0%
- 351
 
8.4%
9 223
 
5.3%
8 108
 
2.6%
4 87
 
2.1%
6 78
 
1.9%
5 67
 
1.6%
Other values (24) 315
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 883
21.1%
2 766
18.3%
765
18.3%
1 546
13.0%
- 351
 
8.4%
9 223
 
5.3%
8 108
 
2.6%
4 87
 
2.1%
6 78
 
1.9%
5 67
 
1.6%
Other values (24) 315
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 883
21.1%
2 766
18.3%
765
18.3%
1 546
13.0%
- 351
 
8.4%
9 223
 
5.3%
8 108
 
2.6%
4 87
 
2.1%
6 78
 
1.9%
5 67
 
1.6%
Other values (24) 315
 
7.5%

allSkills
Text

MISSING 

Distinct551
Distinct (%)100.0%
Missing51
Missing (%)8.5%
Memory size25.6 KiB
2024-06-24T13:35:36.011210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length869
Median length453
Mean length358.52087
Min length15

Characters and Unicode

Total characters197545
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique551 ?
Unique (%)100.0%

Sample

1st rowManagement Consulting, Presentations, Microsoft Dynamics CRM, Scrum, Critical Thinking, Microsoft PowerPoint
2nd rowFICO, ECC, SAP R/3, SAP Implementation, SAP, ERP, SAP FICO, Requirements Analysis, SAP FI, SAP ERP, Business Analysis, IDOC, SAP Products, Consulting, Integration, Financial Reporting, Team Management, Vendor Management
3rd rowMorphX, X++, Microsoft Dynamics ERP, Oracle 10g, Red Hat Linux, Telecom BSS, Microsoft Dynamics AX 4.0, 2009, 2012, 2012 R2, Enterprise Portals, Oracle, Microsoft Dynamics, SSRS, ERP, Solution Architecture, Business Process, Requirements Analysis, SQL, .NET, Business Analysis, Microsoft SQL Server, Enterprise Resource Planning (ERP)
4th rowExternal Audits, Auditing, Tax, Financial Audits, Financial Statement Auditing
5th rowBusiness Process Improvement, Technical Specs, Process Modeling, Problem Solving, Communication, Strategy, Technical Architecture, Business Requirements, Architecture, Agile Methodologies, PL/SQL, Java, Testing, Telecommunications, Requirements Analysis, Service Delivery, Integration, User Acceptance Testing, SDP, Solution Architecture, Test Planning, Software Project Management, Test Automation, Software Development, Software Quality Assurance, Oracle, OSS, Open Source Software, Linux, WebLogic, Quality Center, Weblogic, Unix, SQL, JIRA, SoapUI, HP Quality Center
ValueCountFrequency (%)
management 1574
 
7.3%
business 952
 
4.4%
microsoft 639
 
3.0%
analysis 600
 
2.8%
project 512
 
2.4%
data 428
 
2.0%
process 351
 
1.6%
strategy 333
 
1.6%
it 318
 
1.5%
development 244
 
1.1%
Other values (1844) 15467
72.2%
2024-06-24T13:35:36.844665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20867
 
10.6%
e 18186
 
9.2%
n 14281
 
7.2%
a 12644
 
6.4%
i 11850
 
6.0%
t 11757
 
6.0%
, 10937
 
5.5%
s 10374
 
5.3%
o 9607
 
4.9%
r 9436
 
4.8%
Other values (63) 67606
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20867
 
10.6%
e 18186
 
9.2%
n 14281
 
7.2%
a 12644
 
6.4%
i 11850
 
6.0%
t 11757
 
6.0%
, 10937
 
5.5%
s 10374
 
5.3%
o 9607
 
4.9%
r 9436
 
4.8%
Other values (63) 67606
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20867
 
10.6%
e 18186
 
9.2%
n 14281
 
7.2%
a 12644
 
6.4%
i 11850
 
6.0%
t 11757
 
6.0%
, 10937
 
5.5%
s 10374
 
5.3%
o 9607
 
4.9%
r 9436
 
4.8%
Other values (63) 67606
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20867
 
10.6%
e 18186
 
9.2%
n 14281
 
7.2%
a 12644
 
6.4%
i 11850
 
6.0%
t 11757
 
6.0%
, 10937
 
5.5%
s 10374
 
5.3%
o 9607
 
4.9%
r 9436
 
4.8%
Other values (63) 67606
34.2%

skill1
Text

MISSING 

Distinct318
Distinct (%)57.7%
Missing51
Missing (%)8.5%
Memory size25.6 KiB
2024-06-24T13:35:37.351018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length61
Median length38
Mean length16.417423
Min length1

Characters and Unicode

Total characters9046
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)42.8%

Sample

1st rowManagement Consulting
2nd rowFICO
3rd rowMorphX
4th rowExternal Audits
5th rowBusiness Process Improvement
ValueCountFrequency (%)
management 64
 
5.9%
business 54
 
5.0%
microsoft 40
 
3.7%
analysis 35
 
3.2%
project 26
 
2.4%
data 25
 
2.3%
strategy 24
 
2.2%
it 20
 
1.8%
consulting 19
 
1.7%
office 18
 
1.7%
Other values (326) 761
70.1%
2024-06-24T13:35:38.224898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 886
 
9.8%
n 729
 
8.1%
i 649
 
7.2%
a 621
 
6.9%
t 605
 
6.7%
s 567
 
6.3%
535
 
5.9%
o 511
 
5.6%
r 476
 
5.3%
c 320
 
3.5%
Other values (56) 3147
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 886
 
9.8%
n 729
 
8.1%
i 649
 
7.2%
a 621
 
6.9%
t 605
 
6.7%
s 567
 
6.3%
535
 
5.9%
o 511
 
5.6%
r 476
 
5.3%
c 320
 
3.5%
Other values (56) 3147
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 886
 
9.8%
n 729
 
8.1%
i 649
 
7.2%
a 621
 
6.9%
t 605
 
6.7%
s 567
 
6.3%
535
 
5.9%
o 511
 
5.6%
r 476
 
5.3%
c 320
 
3.5%
Other values (56) 3147
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 886
 
9.8%
n 729
 
8.1%
i 649
 
7.2%
a 621
 
6.9%
t 605
 
6.7%
s 567
 
6.3%
535
 
5.9%
o 511
 
5.6%
r 476
 
5.3%
c 320
 
3.5%
Other values (56) 3147
34.8%

endorsement1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)16.6%
Missing241
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean23.944598
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:38.563818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q326
95-th percentile65
Maximum400
Range399
Interquartile range (IQR)24

Descriptive statistics

Standard deviation55.741589
Coefficient of variation (CV)2.32794
Kurtosis30.62305
Mean23.944598
Median Absolute Deviation (MAD)6
Skewness5.3686813
Sum8644
Variance3107.1247
MonotonicityNot monotonic
2024-06-24T13:35:38.898114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 60
 
10.0%
2 37
 
6.1%
3 31
 
5.1%
5 19
 
3.2%
4 14
 
2.3%
6 11
 
1.8%
7 10
 
1.7%
21 9
 
1.5%
31 9
 
1.5%
10 8
 
1.3%
Other values (50) 153
25.4%
(Missing) 241
40.0%
ValueCountFrequency (%)
1 60
10.0%
2 37
6.1%
3 31
5.1%
4 14
 
2.3%
5 19
 
3.2%
6 11
 
1.8%
7 10
 
1.7%
8 8
 
1.3%
9 8
 
1.3%
10 8
 
1.3%
ValueCountFrequency (%)
400 1
 
0.2%
365 7
1.2%
126 1
 
0.2%
99 1
 
0.2%
97 1
 
0.2%
96 1
 
0.2%
87 1
 
0.2%
76 1
 
0.2%
72 1
 
0.2%
66 2
 
0.3%

skill2
Text

MISSING 

Distinct342
Distinct (%)62.2%
Missing52
Missing (%)8.6%
Memory size25.6 KiB
2024-06-24T13:35:39.385357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length59
Median length31
Mean length15.687273
Min length1

Characters and Unicode

Total characters8628
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)47.1%

Sample

1st rowPresentations
2nd rowECC
3rd rowX++
4th rowAuditing
5th rowTechnical Specs
ValueCountFrequency (%)
management 53
 
5.0%
business 40
 
3.8%
microsoft 35
 
3.3%
it 26
 
2.5%
data 25
 
2.4%
analysis 25
 
2.4%
strategy 22
 
2.1%
project 21
 
2.0%
architecture 15
 
1.4%
sap 13
 
1.2%
Other values (354) 783
74.0%
2024-06-24T13:35:40.381900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 858
 
9.9%
n 644
 
7.5%
a 606
 
7.0%
t 583
 
6.8%
i 575
 
6.7%
508
 
5.9%
r 493
 
5.7%
s 491
 
5.7%
o 483
 
5.6%
c 313
 
3.6%
Other values (51) 3074
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 858
 
9.9%
n 644
 
7.5%
a 606
 
7.0%
t 583
 
6.8%
i 575
 
6.7%
508
 
5.9%
r 493
 
5.7%
s 491
 
5.7%
o 483
 
5.6%
c 313
 
3.6%
Other values (51) 3074
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 858
 
9.9%
n 644
 
7.5%
a 606
 
7.0%
t 583
 
6.8%
i 575
 
6.7%
508
 
5.9%
r 493
 
5.7%
s 491
 
5.7%
o 483
 
5.6%
c 313
 
3.6%
Other values (51) 3074
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 858
 
9.9%
n 644
 
7.5%
a 606
 
7.0%
t 583
 
6.8%
i 575
 
6.7%
508
 
5.9%
r 493
 
5.7%
s 491
 
5.7%
o 483
 
5.6%
c 313
 
3.6%
Other values (51) 3074
35.6%

endorsement2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)15.7%
Missing238
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean17.071429
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:40.947314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q319.25
95-th percentile52
Maximum400
Range399
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation41.213218
Coefficient of variation (CV)2.4141634
Kurtosis60.710065
Mean17.071429
Median Absolute Deviation (MAD)6
Skewness7.3162912
Sum6214
Variance1698.5293
MonotonicityNot monotonic
2024-06-24T13:35:41.480152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 78
 
13.0%
2 43
 
7.1%
3 23
 
3.8%
8 20
 
3.3%
5 16
 
2.7%
7 13
 
2.2%
10 11
 
1.8%
4 11
 
1.8%
21 11
 
1.8%
6 9
 
1.5%
Other values (47) 129
21.4%
(Missing) 238
39.5%
ValueCountFrequency (%)
1 78
13.0%
2 43
7.1%
3 23
 
3.8%
4 11
 
1.8%
5 16
 
2.7%
6 9
 
1.5%
7 13
 
2.2%
8 20
 
3.3%
9 9
 
1.5%
10 11
 
1.8%
ValueCountFrequency (%)
400 1
 
0.2%
365 3
0.5%
102 1
 
0.2%
96 1
 
0.2%
86 1
 
0.2%
75 1
 
0.2%
74 1
 
0.2%
65 2
0.3%
62 1
 
0.2%
61 2
0.3%

skill3
Text

MISSING 

Distinct328
Distinct (%)59.7%
Missing53
Missing (%)8.8%
Memory size25.6 KiB
2024-06-24T13:35:42.920867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length30
Mean length15.710383
Min length1

Characters and Unicode

Total characters8625
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)45.0%

Sample

1st rowMicrosoft Dynamics CRM
2nd rowSAP R/3
3rd rowMicrosoft Dynamics ERP
4th rowTax
5th rowProcess Modeling
ValueCountFrequency (%)
business 60
 
5.8%
management 59
 
5.7%
microsoft 43
 
4.2%
analysis 32
 
3.1%
data 21
 
2.0%
project 19
 
1.8%
development 18
 
1.7%
leadership 17
 
1.6%
process 15
 
1.4%
consulting 15
 
1.4%
Other values (333) 737
71.1%
2024-06-24T13:35:44.278891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 843
 
9.8%
n 694
 
8.0%
i 656
 
7.6%
s 586
 
6.8%
a 578
 
6.7%
t 560
 
6.5%
487
 
5.6%
o 484
 
5.6%
r 451
 
5.2%
l 308
 
3.6%
Other values (55) 2978
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 843
 
9.8%
n 694
 
8.0%
i 656
 
7.6%
s 586
 
6.8%
a 578
 
6.7%
t 560
 
6.5%
487
 
5.6%
o 484
 
5.6%
r 451
 
5.2%
l 308
 
3.6%
Other values (55) 2978
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 843
 
9.8%
n 694
 
8.0%
i 656
 
7.6%
s 586
 
6.8%
a 578
 
6.7%
t 560
 
6.5%
487
 
5.6%
o 484
 
5.6%
r 451
 
5.2%
l 308
 
3.6%
Other values (55) 2978
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 843
 
9.8%
n 694
 
8.0%
i 656
 
7.6%
s 586
 
6.8%
a 578
 
6.7%
t 560
 
6.5%
487
 
5.6%
o 484
 
5.6%
r 451
 
5.2%
l 308
 
3.6%
Other values (55) 2978
34.5%

endorsement3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)16.1%
Missing223
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean21.327177
Minimum1
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:44.622777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q317
95-th percentile51.1
Maximum2019
Range2018
Interquartile range (IQR)15

Descriptive statistics

Standard deviation110.56284
Coefficient of variation (CV)5.1841292
Kurtosis284.56116
Mean21.327177
Median Absolute Deviation (MAD)4
Skewness16.035856
Sum8083
Variance12224.141
MonotonicityNot monotonic
2024-06-24T13:35:44.947776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 76
 
12.6%
2 58
 
9.6%
3 29
 
4.8%
4 20
 
3.3%
5 15
 
2.5%
8 14
 
2.3%
10 12
 
2.0%
7 10
 
1.7%
6 10
 
1.7%
32 8
 
1.3%
Other values (51) 127
21.1%
(Missing) 223
37.0%
ValueCountFrequency (%)
1 76
12.6%
2 58
9.6%
3 29
 
4.8%
4 20
 
3.3%
5 15
 
2.5%
6 10
 
1.7%
7 10
 
1.7%
8 14
 
2.3%
9 3
 
0.5%
10 12
 
2.0%
ValueCountFrequency (%)
2019 1
 
0.2%
400 1
 
0.2%
365 3
0.5%
106 1
 
0.2%
98 1
 
0.2%
96 1
 
0.2%
82 1
 
0.2%
75 1
 
0.2%
73 1
 
0.2%
64 1
 
0.2%

skill4
Text

MISSING 

Distinct331
Distinct (%)60.7%
Missing57
Missing (%)9.5%
Memory size25.6 KiB
2024-06-24T13:35:45.456165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length47
Median length29
Mean length15.222018
Min length1

Characters and Unicode

Total characters8296
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique251 ?
Unique (%)46.1%

Sample

1st rowScrum
2nd rowSAP Implementation
3rd rowOracle 10g
4th rowFinancial Audits
5th rowProblem Solving
ValueCountFrequency (%)
management 85
 
8.4%
business 55
 
5.4%
microsoft 33
 
3.3%
project 31
 
3.1%
analysis 28
 
2.8%
data 24
 
2.4%
process 20
 
2.0%
it 19
 
1.9%
strategy 18
 
1.8%
consulting 16
 
1.6%
Other values (337) 681
67.4%
2024-06-24T13:35:46.256645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 854
 
10.3%
n 681
 
8.2%
a 593
 
7.1%
t 580
 
7.0%
i 572
 
6.9%
s 523
 
6.3%
465
 
5.6%
o 446
 
5.4%
r 420
 
5.1%
c 295
 
3.6%
Other values (53) 2867
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 854
 
10.3%
n 681
 
8.2%
a 593
 
7.1%
t 580
 
7.0%
i 572
 
6.9%
s 523
 
6.3%
465
 
5.6%
o 446
 
5.4%
r 420
 
5.1%
c 295
 
3.6%
Other values (53) 2867
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 854
 
10.3%
n 681
 
8.2%
a 593
 
7.1%
t 580
 
7.0%
i 572
 
6.9%
s 523
 
6.3%
465
 
5.6%
o 446
 
5.4%
r 420
 
5.1%
c 295
 
3.6%
Other values (53) 2867
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 854
 
10.3%
n 681
 
8.2%
a 593
 
7.1%
t 580
 
7.0%
i 572
 
6.9%
s 523
 
6.3%
465
 
5.6%
o 446
 
5.4%
r 420
 
5.1%
c 295
 
3.6%
Other values (53) 2867
34.6%

endorsement4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)14.7%
Missing241
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean209973.15
Minimum1
Maximum75792018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:46.588253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q317
95-th percentile52
Maximum75792018
Range75792017
Interquartile range (IQR)15

Descriptive statistics

Standard deviation3989052.4
Coefficient of variation (CV)18.997916
Kurtosis361
Mean209973.15
Median Absolute Deviation (MAD)5
Skewness19
Sum75800308
Variance1.5912539 × 1013
MonotonicityNot monotonic
2024-06-24T13:35:46.904985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 65
 
10.8%
2 47
 
7.8%
3 33
 
5.5%
4 19
 
3.2%
8 17
 
2.8%
6 15
 
2.5%
7 15
 
2.5%
5 15
 
2.5%
9 10
 
1.7%
12 7
 
1.2%
Other values (43) 118
19.6%
(Missing) 241
40.0%
ValueCountFrequency (%)
1 65
10.8%
2 47
7.8%
3 33
5.5%
4 19
 
3.2%
5 15
 
2.5%
6 15
 
2.5%
7 15
 
2.5%
8 17
 
2.8%
9 10
 
1.7%
10 1
 
0.2%
ValueCountFrequency (%)
75792018 1
0.2%
2019 1
0.2%
1110 1
0.2%
400 1
0.2%
365 1
0.2%
129 1
0.2%
108 1
0.2%
95 1
0.2%
86 1
0.2%
76 1
0.2%

skill5
Text

MISSING 

Distinct311
Distinct (%)57.8%
Missing64
Missing (%)10.6%
Memory size25.6 KiB
2024-06-24T13:35:47.387498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length54
Median length33
Mean length16.501859
Min length1

Characters and Unicode

Total characters8878
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique233 ?
Unique (%)43.3%

Sample

1st rowCritical Thinking
2nd rowSAP
3rd rowRed Hat Linux
4th rowFinancial Statement Auditing
5th rowCommunication
ValueCountFrequency (%)
management 84
 
7.9%
business 50
 
4.7%
analysis 37
 
3.5%
microsoft 31
 
2.9%
it 23
 
2.2%
project 21
 
2.0%
strategy 20
 
1.9%
data 18
 
1.7%
service 16
 
1.5%
financial 16
 
1.5%
Other values (344) 753
70.4%
2024-06-24T13:35:48.225161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 920
 
10.4%
n 736
 
8.3%
a 649
 
7.3%
t 594
 
6.7%
i 583
 
6.6%
531
 
6.0%
s 516
 
5.8%
r 470
 
5.3%
o 466
 
5.2%
c 289
 
3.3%
Other values (52) 3124
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 920
 
10.4%
n 736
 
8.3%
a 649
 
7.3%
t 594
 
6.7%
i 583
 
6.6%
531
 
6.0%
s 516
 
5.8%
r 470
 
5.3%
o 466
 
5.2%
c 289
 
3.3%
Other values (52) 3124
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 920
 
10.4%
n 736
 
8.3%
a 649
 
7.3%
t 594
 
6.7%
i 583
 
6.6%
531
 
6.0%
s 516
 
5.8%
r 470
 
5.3%
o 466
 
5.2%
c 289
 
3.3%
Other values (52) 3124
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 920
 
10.4%
n 736
 
8.3%
a 649
 
7.3%
t 594
 
6.7%
i 583
 
6.6%
531
 
6.0%
s 516
 
5.8%
r 470
 
5.3%
o 466
 
5.2%
c 289
 
3.3%
Other values (52) 3124
35.2%

endorsement5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)13.8%
Missing246
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean13.092697
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:48.573804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q314
95-th percentile47.5
Maximum400
Range399
Interquartile range (IQR)12

Descriptive statistics

Standard deviation31.267944
Coefficient of variation (CV)2.3881974
Kurtosis110.45558
Mean13.092697
Median Absolute Deviation (MAD)4
Skewness9.5858253
Sum4661
Variance977.68434
MonotonicityNot monotonic
2024-06-24T13:35:48.923132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 64
 
10.6%
2 37
 
6.1%
3 33
 
5.5%
4 30
 
5.0%
6 20
 
3.3%
5 19
 
3.2%
7 18
 
3.0%
8 11
 
1.8%
14 8
 
1.3%
21 7
 
1.2%
Other values (39) 109
18.1%
(Missing) 246
40.9%
ValueCountFrequency (%)
1 64
10.6%
2 37
6.1%
3 33
5.5%
4 30
5.0%
5 19
 
3.2%
6 20
 
3.3%
7 18
 
3.0%
8 11
 
1.8%
9 5
 
0.8%
10 7
 
1.2%
ValueCountFrequency (%)
400 1
 
0.2%
365 1
 
0.2%
95 1
 
0.2%
74 1
 
0.2%
65 1
 
0.2%
64 1
 
0.2%
63 1
 
0.2%
54 3
0.5%
53 4
0.7%
52 3
0.5%

skill6
Text

MISSING 

Distinct317
Distinct (%)60.8%
Missing81
Missing (%)13.5%
Memory size25.6 KiB
2024-06-24T13:35:49.476459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length54
Median length30
Mean length15.287908
Min length3

Characters and Unicode

Total characters7965
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)45.1%

Sample

1st rowMicrosoft PowerPoint
2nd rowERP
3rd rowTelecom BSS
4th rowStrategy
5th rowEnterprise Resource Planning (ERP)
ValueCountFrequency (%)
management 63
 
6.4%
business 53
 
5.4%
analysis 38
 
3.9%
microsoft 29
 
3.0%
data 28
 
2.9%
it 18
 
1.8%
process 15
 
1.5%
service 13
 
1.3%
project 13
 
1.3%
leadership 12
 
1.2%
Other values (333) 698
71.2%
2024-06-24T13:35:50.360586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 821
 
10.3%
n 657
 
8.2%
a 608
 
7.6%
i 560
 
7.0%
s 515
 
6.5%
t 500
 
6.3%
459
 
5.8%
r 415
 
5.2%
o 413
 
5.2%
c 262
 
3.3%
Other values (50) 2755
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 821
 
10.3%
n 657
 
8.2%
a 608
 
7.6%
i 560
 
7.0%
s 515
 
6.5%
t 500
 
6.3%
459
 
5.8%
r 415
 
5.2%
o 413
 
5.2%
c 262
 
3.3%
Other values (50) 2755
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 821
 
10.3%
n 657
 
8.2%
a 608
 
7.6%
i 560
 
7.0%
s 515
 
6.5%
t 500
 
6.3%
459
 
5.8%
r 415
 
5.2%
o 413
 
5.2%
c 262
 
3.3%
Other values (50) 2755
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 821
 
10.3%
n 657
 
8.2%
a 608
 
7.6%
i 560
 
7.0%
s 515
 
6.5%
t 500
 
6.3%
459
 
5.8%
r 415
 
5.2%
o 413
 
5.2%
c 262
 
3.3%
Other values (50) 2755
34.6%

endorsement6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)14.2%
Missing256
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean14.459538
Minimum1
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:50.715160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile46.25
Maximum900
Range899
Interquartile range (IQR)10

Descriptive statistics

Standard deviation54.33823
Coefficient of variation (CV)3.7579508
Kurtosis210.37116
Mean14.459538
Median Absolute Deviation (MAD)4
Skewness13.519066
Sum5003
Variance2952.6433
MonotonicityNot monotonic
2024-06-24T13:35:51.470720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 67
 
11.1%
2 42
 
7.0%
3 31
 
5.1%
4 26
 
4.3%
5 19
 
3.2%
6 19
 
3.2%
9 13
 
2.2%
7 13
 
2.2%
13 10
 
1.7%
10 9
 
1.5%
Other values (39) 97
 
16.1%
(Missing) 256
42.5%
ValueCountFrequency (%)
1 67
11.1%
2 42
7.0%
3 31
5.1%
4 26
 
4.3%
5 19
 
3.2%
6 19
 
3.2%
7 13
 
2.2%
8 9
 
1.5%
9 13
 
2.2%
10 9
 
1.5%
ValueCountFrequency (%)
900 1
0.2%
365 1
0.2%
162 1
0.2%
107 1
0.2%
99 1
0.2%
96 1
0.2%
88 1
0.2%
85 1
0.2%
65 1
0.2%
63 1
0.2%

error
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing602
Missing (%)100.0%
Memory size25.6 KiB
Distinct95
Distinct (%)15.9%
Missing5
Missing (%)0.8%
Memory size25.6 KiB
Minimum1996-11-01 00:00:00
Maximum2024-05-01 00:00:00
2024-06-24T13:35:52.291532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:52.621415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct588
Distinct (%)98.5%
Missing5
Missing (%)0.8%
Memory size25.6 KiB
Minimum2015-06-01 00:00:00
Maximum2024-06-24 12:46:00.195382
2024-06-24T13:35:52.948984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:53.338503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

jobTenureYears
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)18.9%
Missing5
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.600938
Minimum0.08
Maximum27.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2024-06-24T13:35:53.681121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.39
Q11.06
median1.98
Q32.73
95-th percentile7.98
Maximum27.64
Range27.56
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation3.0487808
Coefficient of variation (CV)1.1721851
Kurtosis23.704152
Mean2.600938
Median Absolute Deviation (MAD)0.75
Skewness4.2467998
Sum1552.76
Variance9.2950642
MonotonicityNot monotonic
2024-06-24T13:35:53.986047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98 50
 
8.3%
1.98 39
 
6.5%
1.39 38
 
6.3%
2.73 34
 
5.6%
0.48 28
 
4.7%
1.48 26
 
4.3%
2.48 24
 
4.0%
0.39 23
 
3.8%
1.73 23
 
3.8%
1.9 14
 
2.3%
Other values (103) 298
49.5%
ValueCountFrequency (%)
0.08 1
 
0.2%
0.15 3
 
0.5%
0.17 2
 
0.3%
0.23 5
 
0.8%
0.25 4
 
0.7%
0.31 1
 
0.2%
0.34 1
 
0.2%
0.39 23
3.8%
0.48 28
4.7%
0.5 1
 
0.2%
ValueCountFrequency (%)
27.64 1
0.2%
25.31 1
0.2%
24.98 1
0.2%
20.48 1
0.2%
18.48 1
0.2%
18.16 1
0.2%
17.81 1
0.2%
16.39 1
0.2%
16.31 1
0.2%
14.23 1
0.2%

schoolStartDate
Date

MISSING 

Distinct81
Distinct (%)16.0%
Missing97
Missing (%)16.1%
Memory size25.6 KiB
Minimum1972-01-01 00:00:00
Maximum2025-01-01 00:00:00
2024-06-24T13:35:54.354833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:55.001588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

schoolEndDate
Date

MISSING 

Distinct101
Distinct (%)20.0%
Missing97
Missing (%)16.1%
Memory size25.6 KiB
Minimum1975-01-01 00:00:00
Maximum2029-12-01 00:00:00
2024-06-24T13:35:55.595453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:56.203456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

schoolStartDate2
Date

MISSING 

Distinct67
Distinct (%)18.1%
Missing232
Missing (%)38.5%
Memory size25.6 KiB
Minimum1970-01-01 00:00:00
Maximum2024-02-01 00:00:00
2024-06-24T13:35:56.868415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:57.499490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

schoolEndDate2
Date

MISSING 

Distinct89
Distinct (%)24.1%
Missing232
Missing (%)38.5%
Memory size25.6 KiB
Minimum1972-01-01 00:00:00
Maximum2024-06-24 12:46:00.278968
2024-06-24T13:35:58.159670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:58.710804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

degreeCategory
Categorical

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
Bachelor
241 
Diploma
239 
High School
61 
Unknown
56 
Master
 
4

Length

Max length11
Median length8
Mean length7.7923588
Min length3

Characters and Unicode

Total characters4691
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowBachelor
2nd rowBachelor
3rd rowBachelor
4th rowBachelor
5th rowPhD

Common Values

ValueCountFrequency (%)
Bachelor 241
40.0%
Diploma 239
39.7%
High School 61
 
10.1%
Unknown 56
 
9.3%
Master 4
 
0.7%
PhD 1
 
0.2%

Length

2024-06-24T13:35:59.306636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T13:35:59.868711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bachelor 241
36.3%
diploma 239
36.0%
high 61
 
9.2%
school 61
 
9.2%
unknown 56
 
8.4%
master 4
 
0.6%
phd 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 658
14.0%
l 541
11.5%
a 484
10.3%
h 364
 
7.8%
c 302
 
6.4%
i 300
 
6.4%
e 245
 
5.2%
r 245
 
5.2%
B 241
 
5.1%
D 240
 
5.1%
Other values (14) 1071
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 658
14.0%
l 541
11.5%
a 484
10.3%
h 364
 
7.8%
c 302
 
6.4%
i 300
 
6.4%
e 245
 
5.2%
r 245
 
5.2%
B 241
 
5.1%
D 240
 
5.1%
Other values (14) 1071
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 658
14.0%
l 541
11.5%
a 484
10.3%
h 364
 
7.8%
c 302
 
6.4%
i 300
 
6.4%
e 245
 
5.2%
r 245
 
5.2%
B 241
 
5.1%
D 240
 
5.1%
Other values (14) 1071
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 658
14.0%
l 541
11.5%
a 484
10.3%
h 364
 
7.8%
c 302
 
6.4%
i 300
 
6.4%
e 245
 
5.2%
r 245
 
5.2%
B 241
 
5.1%
D 240
 
5.1%
Other values (14) 1071
22.8%

degreeCategory2
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
Unknown
243 
Diploma
147 
Bachelor
145 
High School
59 
Master
 
5
Other values (2)
 
3

Length

Max length11
Median length7
Mean length7.6146179
Min length3

Characters and Unicode

Total characters4584
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowBachelor
2nd rowBachelor
3rd rowBachelor
4th rowHigh School
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 243
40.4%
Diploma 147
24.4%
Bachelor 145
24.1%
High School 59
 
9.8%
Master 5
 
0.8%
PhD 2
 
0.3%
Associate 1
 
0.2%

Length

2024-06-24T13:36:00.397722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T13:36:00.892613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown 243
36.8%
diploma 147
22.2%
bachelor 145
21.9%
high 59
 
8.9%
school 59
 
8.9%
master 5
 
0.8%
phd 2
 
0.3%
associate 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 729
15.9%
o 654
14.3%
l 351
 
7.7%
a 298
 
6.5%
h 265
 
5.8%
U 243
 
5.3%
k 243
 
5.3%
w 243
 
5.3%
i 207
 
4.5%
c 205
 
4.5%
Other values (15) 1146
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 729
15.9%
o 654
14.3%
l 351
 
7.7%
a 298
 
6.5%
h 265
 
5.8%
U 243
 
5.3%
k 243
 
5.3%
w 243
 
5.3%
i 207
 
4.5%
c 205
 
4.5%
Other values (15) 1146
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 729
15.9%
o 654
14.3%
l 351
 
7.7%
a 298
 
6.5%
h 265
 
5.8%
U 243
 
5.3%
k 243
 
5.3%
w 243
 
5.3%
i 207
 
4.5%
c 205
 
4.5%
Other values (15) 1146
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 729
15.9%
o 654
14.3%
l 351
 
7.7%
a 298
 
6.5%
h 265
 
5.8%
U 243
 
5.3%
k 243
 
5.3%
w 243
 
5.3%
i 207
 
4.5%
c 205
 
4.5%
Other values (15) 1146
25.0%

fieldCategory
Categorical

Distinct8
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
Health
210 
IT
167 
Data
74 
Science
60 
Unknown
56 
Other values (3)
35 

Length

Max length11
Median length8
Mean length4.9152824
Min length2

Characters and Unicode

Total characters2959
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth
2nd rowScience
3rd rowIT
4th rowIT
5th rowBusiness

Common Values

ValueCountFrequency (%)
Health 210
34.9%
IT 167
27.7%
Data 74
 
12.3%
Science 60
 
10.0%
Unknown 56
 
9.3%
Business 17
 
2.8%
Arts 11
 
1.8%
Engineering 7
 
1.2%

Length

2024-06-24T13:36:01.410033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T13:36:01.942910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
health 210
34.9%
it 167
27.7%
data 74
 
12.3%
science 60
 
10.0%
unknown 56
 
9.3%
business 17
 
2.8%
arts 11
 
1.8%
engineering 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 361
12.2%
a 358
12.1%
t 295
10.0%
n 266
9.0%
h 210
 
7.1%
H 210
 
7.1%
l 210
 
7.1%
I 167
 
5.6%
T 167
 
5.6%
c 120
 
4.1%
Other values (14) 595
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 361
12.2%
a 358
12.1%
t 295
10.0%
n 266
9.0%
h 210
 
7.1%
H 210
 
7.1%
l 210
 
7.1%
I 167
 
5.6%
T 167
 
5.6%
c 120
 
4.1%
Other values (14) 595
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 361
12.2%
a 358
12.1%
t 295
10.0%
n 266
9.0%
h 210
 
7.1%
H 210
 
7.1%
l 210
 
7.1%
I 167
 
5.6%
T 167
 
5.6%
c 120
 
4.1%
Other values (14) 595
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 361
12.2%
a 358
12.1%
t 295
10.0%
n 266
9.0%
h 210
 
7.1%
H 210
 
7.1%
l 210
 
7.1%
I 167
 
5.6%
T 167
 
5.6%
c 120
 
4.1%
Other values (14) 595
20.1%

fieldCategory2
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
Unknown
243 
Health
165 
IT
86 
Data
32 
Science
32 
Other values (4)
44 

Length

Max length11
Median length8
Mean length5.948505
Min length2

Characters and Unicode

Total characters3581
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowHealth
2nd rowEngineering
3rd rowIT
4th rowEngineering
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 243
40.4%
Health 165
27.4%
IT 86
 
14.3%
Data 32
 
5.3%
Science 32
 
5.3%
Business 18
 
3.0%
Engineering 17
 
2.8%
Arts 8
 
1.3%
Law 1
 
0.2%

Length

2024-06-24T13:36:02.259911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T13:36:02.567331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown 243
40.4%
health 165
27.4%
it 86
 
14.3%
data 32
 
5.3%
science 32
 
5.3%
business 18
 
3.0%
engineering 17
 
2.8%
arts 8
 
1.3%
law 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 830
23.2%
e 281
 
7.8%
w 244
 
6.8%
U 243
 
6.8%
k 243
 
6.8%
o 243
 
6.8%
a 230
 
6.4%
t 205
 
5.7%
H 165
 
4.6%
l 165
 
4.6%
Other values (15) 732
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 830
23.2%
e 281
 
7.8%
w 244
 
6.8%
U 243
 
6.8%
k 243
 
6.8%
o 243
 
6.8%
a 230
 
6.4%
t 205
 
5.7%
H 165
 
4.6%
l 165
 
4.6%
Other values (15) 732
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 830
23.2%
e 281
 
7.8%
w 244
 
6.8%
U 243
 
6.8%
k 243
 
6.8%
o 243
 
6.8%
a 230
 
6.4%
t 205
 
5.7%
H 165
 
4.6%
l 165
 
4.6%
Other values (15) 732
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 830
23.2%
e 281
 
7.8%
w 244
 
6.8%
U 243
 
6.8%
k 243
 
6.8%
o 243
 
6.8%
a 230
 
6.4%
t 205
 
5.7%
H 165
 
4.6%
l 165
 
4.6%
Other values (15) 732
20.4%

weighted_skills
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size25.6 KiB

totalExperienceYears
Real number (ℝ)

MISSING  ZEROS 

Distinct83
Distinct (%)16.3%
Missing93
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean10.85057
Minimum-5.44
Maximum49.48
Zeros20
Zeros (%)3.3%
Negative9
Negative (%)1.5%
Memory size25.6 KiB
2024-06-24T13:36:02.867536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.44
5-th percentile0
Q13.48
median8.48
Q316.48
95-th percentile29.08
Maximum49.48
Range54.92
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.373172
Coefficient of variation (CV)0.86384146
Kurtosis0.85495125
Mean10.85057
Median Absolute Deviation (MAD)6
Skewness1.0896277
Sum5522.94
Variance87.856353
MonotonicityNot monotonic
2024-06-24T13:36:03.154673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.48 40
 
6.6%
4.48 30
 
5.0%
9.48 26
 
4.3%
2.48 22
 
3.7%
6.48 22
 
3.7%
8.48 20
 
3.3%
0 20
 
3.3%
7.48 17
 
2.8%
5.48 17
 
2.8%
10.48 17
 
2.8%
Other values (73) 278
46.2%
(Missing) 93
 
15.4%
ValueCountFrequency (%)
-5.44 1
 
0.2%
-2.52 1
 
0.2%
-1.77 1
 
0.2%
-0.85 1
 
0.2%
-0.52 2
 
0.3%
-0.44 2
 
0.3%
-0.02 1
 
0.2%
0 20
3.3%
0.15 2
 
0.3%
0.48 2
 
0.3%
ValueCountFrequency (%)
49.48 1
 
0.2%
43.48 1
 
0.2%
41.48 2
0.3%
40.48 1
 
0.2%
39.48 1
 
0.2%
37.48 1
 
0.2%
36.48 2
0.3%
34.48 3
0.5%
33.48 1
 
0.2%
32.48 3
0.5%

jobTenureCategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.5%
Missing5
Missing (%)0.8%
Memory size25.6 KiB
Mid-level
217 
Senior
211 
Junior
169 

Length

Max length9
Median length6
Mean length7.0904523
Min length6

Characters and Unicode

Total characters4233
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJunior
2nd rowJunior
3rd rowMid-level
4th rowMid-level
5th rowJunior

Common Values

ValueCountFrequency (%)
Mid-level 217
36.0%
Senior 211
35.0%
Junior 169
28.1%
(Missing) 5
 
0.8%

Length

2024-06-24T13:36:03.486722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T13:36:03.755275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mid-level 217
36.3%
senior 211
35.3%
junior 169
28.3%

Most occurring characters

ValueCountFrequency (%)
e 645
15.2%
i 597
14.1%
l 434
10.3%
n 380
9.0%
o 380
9.0%
r 380
9.0%
M 217
 
5.1%
d 217
 
5.1%
- 217
 
5.1%
v 217
 
5.1%
Other values (3) 549
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 645
15.2%
i 597
14.1%
l 434
10.3%
n 380
9.0%
o 380
9.0%
r 380
9.0%
M 217
 
5.1%
d 217
 
5.1%
- 217
 
5.1%
v 217
 
5.1%
Other values (3) 549
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 645
15.2%
i 597
14.1%
l 434
10.3%
n 380
9.0%
o 380
9.0%
r 380
9.0%
M 217
 
5.1%
d 217
 
5.1%
- 217
 
5.1%
v 217
 
5.1%
Other values (3) 549
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 645
15.2%
i 597
14.1%
l 434
10.3%
n 380
9.0%
o 380
9.0%
r 380
9.0%
M 217
 
5.1%
d 217
 
5.1%
- 217
 
5.1%
v 217
 
5.1%
Other values (3) 549
13.0%

Interactions

2024-06-24T13:35:07.287758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:50.350414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:52.455018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:54.594280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:57.700688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:00.941797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:03.136362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:05.104925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:07.568615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:50.615082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:52.729075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:54.950616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:58.063175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:01.250459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:03.403996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:05.424383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:07.824805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:50.900889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:52.996818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:55.372259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:58.425009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:01.509410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:03.648323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:05.684006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:08.101516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:51.193535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:53.292934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:55.770215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:58.819467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:01.774721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:03.895604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:05.957454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:08.363865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:51.439940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:53.542007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:56.172186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:59.233492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:02.047089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:04.164389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:06.235211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:08.648660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:51.701717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:53.823970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:56.527401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:59.633331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:02.365171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:04.433660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:06.509702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:08.874218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:51.951686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:54.067649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:56.882414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:59.984046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:02.591749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:04.640722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:06.741127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:09.173057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:52.207791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:54.335646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:34:57.301888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:00.695306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:02.855072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:04.870280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-24T13:35:06.996218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-24T13:36:03.973227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
companydegreeCategorydegreeCategory2endorsement1endorsement2endorsement3endorsement4endorsement5endorsement6fieldCategoryfieldCategory2jobTenureCategoryjobTenureYearsjobTitletotalExperienceYears
company1.0000.0000.409-0.036-0.021-0.035-0.047-0.034-0.0770.1630.1090.0760.0380.0000.037
degreeCategory0.0001.0000.1140.0320.0300.0510.0560.094-0.0510.4910.1190.0500.0570.025-0.116
degreeCategory20.4090.1141.000-0.052-0.0340.013-0.050-0.000-0.0510.1070.5060.000-0.0120.0000.103
endorsement1-0.0360.032-0.0521.0000.7230.6500.5610.5580.4490.0000.0800.0860.1330.0810.279
endorsement2-0.0210.030-0.0340.7231.0000.6950.6460.5590.5140.0000.0000.0000.1020.0190.292
endorsement3-0.0350.0510.0130.6500.6951.0000.6630.6010.5280.0590.0000.0530.0870.0820.270
endorsement4-0.0470.056-0.0500.5610.6460.6631.0000.6710.6470.0000.0000.0000.0990.0480.258
endorsement5-0.0340.094-0.0000.5580.5590.6010.6711.0000.5500.1590.0000.0200.0770.0720.268
endorsement6-0.077-0.051-0.0510.4490.5140.5280.6470.5501.0000.0000.0000.0000.1050.1090.244
fieldCategory0.1630.4910.1070.0000.0000.0590.0000.1590.0001.0000.0980.0950.0750.0120.087
fieldCategory20.1090.1190.5060.0800.0000.0000.0000.0000.0000.0981.0000.070-0.0260.0720.145
jobTenureCategory0.0760.0500.0000.0860.0000.0530.0000.0200.0000.0950.0701.0000.9420.1880.356
jobTenureYears0.0380.057-0.0120.1330.1020.0870.0990.0770.1050.075-0.0260.9421.0000.1360.403
jobTitle0.0000.0250.0000.0810.0190.0820.0480.0720.1090.0120.0720.1880.1361.000-0.165
totalExperienceYears0.037-0.1160.1030.2790.2920.2700.2580.2680.2440.0870.1450.3560.403-0.1651.000

Missing values

2024-06-24T13:35:09.697916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-24T13:35:11.444233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-24T13:35:13.675615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

locationfullNamecompanyjobTitlejobLocationjobDateRangejobStartedSincejobDurationcompany2jobTitle2jobDateRange2jobStartedSince2schoolschoolUrlschoolDateRangeschoolUrl2schoolDegree2schoolDateRange2allSkillsskill1endorsement1skill2endorsement2skill3endorsement3skill4endorsement4skill5endorsement5skill6endorsement6errorjobStartDatejobEndDatejobTenureYearsschoolStartDateschoolEndDateschoolStartDate2schoolEndDate2degreeCategorydegreeCategory2fieldCategoryfieldCategory2weighted_skillstotalExperienceYearsjobTenureCategory
2Greater Melbourne AreaWill AdamsKPMG AustraliaSenior ConsultantMelbourne, Victoria, AustraliaOct 2020 - Feb 20222020-10-011 yr 5 mosKPMG AustraliaConsultantSep 2018 - Oct 20202018-09-01Monash Universityhttps://www.linkedin.com/company/5663/2012 - 2016https://www.linkedin.com/company/5663/Bachelor's degree, Business (Marketing) (International Business)2012 - 2016Management Consulting, Presentations, Microsoft Dynamics CRM, Scrum, Critical Thinking, Microsoft PowerPointManagement Consulting4.0Presentations7.0Microsoft Dynamics CRM5.0Scrum4.0Critical Thinking4.0Microsoft PowerPoint4.0NaN2020-10-012022-02-01 00:00:00.0000001.342012-01-012016-01-012012-01-012016-01-01BachelorBachelorHealthHealth{'Management Consulting': 4.0, 'Presentations': 7.0, 'Microsoft Dynamics CRM': 5.0, 'Scrum': 4.0, 'Critical Thinking': 4.0, 'Microsoft PowerPoint': 4.0}8.48Junior
4Melbourne, VictoriaAmmrith AdithyaKPMGConsultantMelbourne, Victoria, AustraliaFeb 2024 - Present2024-02-015 mosCommonwealth BankEarly Customer Engagement Specialist (Financial Assist Solutions)Mar 2020 - Aug 20202020-03-01Monash Universityhttps://www.linkedin.com/company/5663/2019 - 2023NaNVCE2015 - 2018NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-02-012024-06-24 12:46:00.1843540.392019-01-012023-01-012015-01-012018-01-01BachelorBachelorScienceEngineering{}1.48Junior
6Greater Sydney AreaPrabin AgarwalKPMG AustraliaAssociate DirectorSydney, New South Wales, AustraliaMar 2022 - Present2022-03-012 yrs 4 mosAccenture AustraliaManagerOct 2016 - Feb 20222016-10-01Institute of Chartered Accountant of IndiaNaN1999 - 2004https://www.linkedin.com/company/633270/Bachelor of Commerce (B.Com.), Accountancy1999 - 2002FICO, ECC, SAP R/3, SAP Implementation, SAP, ERP, SAP FICO, Requirements Analysis, SAP FI, SAP ERP, Business Analysis, IDOC, SAP Products, Consulting, Integration, Financial Reporting, Team Management, Vendor ManagementFICO1.0ECCNaNSAP R/31.0SAP Implementation1.0SAP10.0ERP6.0NaN2022-03-012024-06-24 12:46:00.1843782.321999-01-012004-01-011999-01-012002-01-01BachelorBachelorITIT{'FICO': 1.0, 'SAP R/3': 1.0, 'SAP Implementation': 1.0, 'SAP': 10.0, 'ERP': 6.0}20.48Mid-level
7Melbourne, Victoria, AustraliaPriyanka AgarwalKPMG AustraliaAssociate DirectorMelbourne, Victoria, AustraliaMay 2022 - Present2022-05-012 yrs 2 mosDXC TechnologySenior Technical ConsultantSep 2021 - May 20222021-09-01College Of Engineering RoorkeeNaN2002 - 2006NaNSSC, PCM1999 - 2002MorphX, X++, Microsoft Dynamics ERP, Oracle 10g, Red Hat Linux, Telecom BSS, Microsoft Dynamics AX 4.0, 2009, 2012, 2012 R2, Enterprise Portals, Oracle, Microsoft Dynamics, SSRS, ERP, Solution Architecture, Business Process, Requirements Analysis, SQL, .NET, Business Analysis, Microsoft SQL Server, Enterprise Resource Planning (ERP)MorphX2.0X++9.0Microsoft Dynamics ERP4.0Oracle 10g1.0Red Hat Linux2.0Telecom BSS2.0NaN2022-05-012024-06-24 12:46:00.1843972.152002-01-012006-01-011999-01-012002-01-01BachelorHigh SchoolITEngineering{'MorphX': 2.0, 'X++': 9.0, 'Microsoft Dynamics ERP': 4.0, 'Oracle 10g': 1.0, 'Red Hat Linux': 2.0, 'Telecom BSS': 2.0}18.48Mid-level
8Delhi, IndiaAkansha AggarwalKPMGManagerDelhi, IndiaSep 2023 - Present2023-09-0110 mosKPMGExecutiveSep 2022 - Sep 20232022-09-01The Institute of Chartered Accountants of Indiahttps://www.linkedin.com/company/1968486/2012 - 2018NaNNaNNaNExternal Audits, Auditing, Tax, Financial Audits, Financial Statement AuditingExternal AuditsNaNAuditingNaNTaxNaNFinancial AuditsNaNFinancial Statement AuditingNaNNaNNaNNaN2023-09-012024-06-24 12:46:00.1844150.812012-01-012018-01-01NaTNaTPhDUnknownBusinessUnknown{}6.48Junior
9Greater Melbourne AreaPallavi AggarwalKPMG AustraliaManagerNaNSep 2022 - Present2022-09-011 yr 10 mosInfosys ConsultingConsultantFeb 2021 - Aug 20222021-02-01Birla Institute of Technology and Science, Pilanihttps://www.linkedin.com/company/739903/2004 - 2008https://www.linkedin.com/company/9584148/BCA, Computer Applications2001 - 2004Business Process Improvement, Technical Specs, Process Modeling, Problem Solving, Communication, Strategy, Technical Architecture, Business Requirements, Architecture, Agile Methodologies, PL/SQL, Java, Testing, Telecommunications, Requirements Analysis, Service Delivery, Integration, User Acceptance Testing, SDP, Solution Architecture, Test Planning, Software Project Management, Test Automation, Software Development, Software Quality Assurance, Oracle, OSS, Open Source Software, Linux, WebLogic, Quality Center, Weblogic, Unix, SQL, JIRA, SoapUI, HP Quality CenterBusiness Process ImprovementNaNTechnical Specs43.0Process Modeling32.0Problem Solving54.0Communication21.0StrategyNaNNaN2022-09-012024-06-24 12:46:00.1844311.812004-01-012008-01-012001-01-012004-01-01BachelorBachelorITData{'Technical Specs': 43.0, 'Process Modeling': 32.0, 'Problem Solving': 54.0, 'Communication': 21.0}16.48Mid-level
10Sydney, New South Wales, AustraliaSaurabh AgrahariKPMG AustraliaAssociate DirectorSydney, New South Wales, Australia · On-siteApr 2023 - Present2023-04-011 yr 3 mosTechwaveDirector - Data Analytics & EPMJun 2020 - Mar 20232020-06-01Great Lakes Institute of Managementhttps://www.linkedin.com/company/363736/2007 - 2008https://www.linkedin.com/company/15109269/Bachelor of Technology (B.Tech.), Information Technology1999 - 2003SAP Datasphere, Pre-sales, Business Analysis, Project Management, Program Management, Enterprise Resource Planning (ERP), Business Intelligence (BI), Data Analysis, Business Analytics, Strategic Planning, Project Planning, Financial Analysis, Financial Planning, Financial Forecasting, FP&A, Software Project Management, Agile Project Management, Cloud Computing, SAP BI, SAP BW, SAP ERP, Business Objects, SAP S/4HANA, SAP Analytics Cloud, SAP Project Management, EPM, SAP BPC, SAP HANA, anaplan, Planning Budgeting & Forecasting, Predictive Analytics, Anaplan, Trusted Business Partner, Interpersonal Skills, Design Thinking, Agile & Waterfall Methodologies, Agile Application Development, Process Improvement, Innovation Management, Problem SolvingSAP DatasphereNaNPre-sales35.0Business Analysis17.0Project Management11.0Program Management1.0Enterprise Resource Planning (ERP)8.0NaN2023-04-012024-06-24 12:46:00.1844471.232007-01-012008-01-011999-01-012003-01-01BachelorBachelorDataIT{'Pre-sales': 35.0, 'Business Analysis': 17.0, 'Project Management': 11.0, 'Program Management': 1.0, 'Enterprise Resource Planning (ERP)': 8.0}16.48Junior
14Greater Melbourne AreaTyler AitkenKPMG AustraliaManagerMelbourne, Victoria, AustraliaOct 2020 - Present2020-10-013 yrs 9 mosKPMG AustraliaSenior Consultant (Digital Delta)Oct 2018 - Sep 20202018-10-01Chartered Accountants Australia and New Zealandhttps://www.linkedin.com/company/654969/2018 - 2020https://www.linkedin.com/company/5663/Double Degree, Bachelor of Commerce and Bachelor of Business Information Systems2012 - 2015Microsoft Office, Finance, Strategy, Analysis, Business Analysis, Financial Analysis, Risk Management, Teamwork, Management, Project Delivery, Business Process Improvement, Microsoft ExcelMicrosoft Office1.0Finance1.0Strategy1.0Analysis1.0Business Analysis1.0Financial AnalysisNaNNaN2020-10-012024-06-24 12:46:00.1844633.732018-01-012020-01-012012-01-012015-01-01BachelorBachelorHealthHealth{'Microsoft Office': 1.0, 'Finance': 1.0, 'Strategy': 1.0, 'Analysis': 1.0, 'Business Analysis': 1.0}4.48Senior
15Greater Melbourne AreaLuis AjeroKPMG AustraliaSenior ConsultantMelbourne, Victoria, AustraliaApr 2022 - Present2022-04-012 yrs 3 mosAppirioAssociate Technical ConsultantOct 2019 - Apr 20222019-10-01University of Melbournehttps://www.linkedin.com/company/5677/Feb 2015 - Nov 2018NaNNaNNaNPython, JavaScript, Java, Microsoft Office, Microsoft PowerPoint, HTML, Git, NodeJs, SQL, English, Haskell, Mean Stack, Express.jsPython1.0JavaScript2.0Java2.0Microsoft Office1.0Microsoft PowerPoint1.0HTML2.0NaN2022-04-012024-06-24 12:46:00.1844782.232015-02-012018-11-01NaTNaTDiplomaUnknownITUnknown{'Python': 1.0, 'JavaScript': 2.0, 'Java': 2.0, 'Microsoft Office': 1.0, 'Microsoft PowerPoint': 1.0, 'HTML': 2.0}5.65Mid-level
16Greater Sydney AreaPhilip AlavaKPMG AustraliaSenior ConsultantNaNJul 2023 - Present2023-07-011 yrKPMG AustraliaSenior Consultant | Transformation Program ManagementJun 2022 - Jul 20232022-06-01UNSWhttps://www.linkedin.com/company/6096/2018 - 2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-07-012024-06-24 12:46:00.1844940.982018-01-012021-01-01NaTNaTBachelorUnknownITUnknown{}3.48Junior
locationfullNamecompanyjobTitlejobLocationjobDateRangejobStartedSincejobDurationcompany2jobTitle2jobDateRange2jobStartedSince2schoolschoolUrlschoolDateRangeschoolUrl2schoolDegree2schoolDateRange2allSkillsskill1endorsement1skill2endorsement2skill3endorsement3skill4endorsement4skill5endorsement5skill6endorsement6errorjobStartDatejobEndDatejobTenureYearsschoolStartDateschoolEndDateschoolStartDate2schoolEndDate2degreeCategorydegreeCategory2fieldCategoryfieldCategory2weighted_skillstotalExperienceYearsjobTenureCategory
1060Greater Sydney AreaRicky YuenKPMGConsultantNaNFeb 2023 - Present2023-02-011 yr 5 mosEYAudit & Assurance Vacationer at EYJan 2022 - Feb 20222022-01-01UNSWhttps://www.linkedin.com/company/6096/2018 - 2022NaNNaNNaNInterpersonal Skills, Time-efficientInterpersonal Skills2.0Time-efficient2.0NaNNaNNaNNaNNaNNaNNaNNaNNaN2023-02-012024-06-24 12:46:00.1952351.392018-01-012022-01-01NaTNaTBachelorUnknownITUnknown{'Interpersonal Skills': 2.0, 'Time-efficient': 2.0}2.48Mid-level
1061Greater Sydney AreaSumedha ZadooKPMG AustraliaAssociate DirectorSydney, New South Wales, AustraliaOct 2021 - Present2021-10-012 yrs 9 mosKPMG AustraliaManager | Management ConsultingOct 2019 - Sep 20212019-10-01University of Technology Sydneyhttps://www.linkedin.com/company/166678/2014 - 2015NaNMaster of Engineering Management, Engineering Management2013 - 2014Requirements Gathering, Management Consulting, Requirements Analysis, Agile Methodologies, Business Strategy, Use Case Analysis, Microsoft SQL Server, Business Process Improvement, FRD, BRDs, Workshop Facilitation, Lean Six Sigma, Project Management, SQL, Management, ASP.NET, Testing, Data Analysis, Databases, MS Project, Project Planning, SDLC, Business Planning, Microsoft BI Suite, Digital Marketing, Market Research, Business Intelligence, SSRS, Primavera P6, Microsoft Project, HTML, MySQL, PL/SQL, Microsoft Office, Microsoft ExcelRequirements Gathering19.0Management Consulting11.0Requirements Analysis19.0Agile Methodologies11.0Business Strategy8.0Use Case Analysis5.0NaN2021-10-012024-06-24 12:46:00.1952502.732014-01-012015-01-012013-01-012014-01-01BachelorDiplomaITHealth{'Requirements Gathering': 19.0, 'Management Consulting': 11.0, 'Requirements Analysis': 19.0, 'Agile Methodologies': 11.0, 'Business Strategy': 8.0, 'Use Case Analysis': 5.0}9.48Senior
1066Canberra, Australian Capital Territory, AustraliaMichelle ZeitlhoferKPMG AustraliaConsultantCanberra, Australian Capital Territory, AustraliaFeb 2023 - Present2023-02-011 yr 5 mosHireupDisability Support WorkerJan 2019 - Feb 20232019-01-01University of Canberrahttps://www.linkedin.com/company/19034/2019 - 2022https://www.linkedin.com/company/19034/Bachelor's degree, Event and Tourism Management2019 - 2022Microsoft Office, Volunteering, Customer Service, Communication, Professional DevelopmentMicrosoft OfficeNaNVolunteeringNaNCustomer ServiceNaNCommunication1.0Professional DevelopmentNaNNaNNaNNaN2023-02-012024-06-24 12:46:00.1952651.392019-01-012022-01-012019-01-012022-01-01High SchoolDiplomaHealthHealth{'Communication': 1.0}2.48Mid-level
1067Sydney, New South Wales, AustraliaCharmaine ZetsKPMGDirectorNaNOct 2023 - Present2023-10-019 mosKPMGDigital Transformation LeadFeb 2021 - Sep 20232021-02-01AGSM @ UNSW Business Schoolhttps://www.linkedin.com/company/15104530/2009 - 2012NaNCertification, Agile2017 - 2017Business Transformation, Program Management, Risk Management, Project Management, Performance Management, Business Process Improvement, Financial Analysis, Business Strategy, Financial Modeling, Change Management, Strategic Planning, Management Consulting, Project Coordination, Strategy, Leadership, Stakeholder Engagement, Stakeholder Management, Digital transformationBusiness Transformation16.0Program Management15.0Risk Management12.0Project Management13.0Performance Management8.0Business Process Improvement12.0NaN2023-10-012024-06-24 12:46:00.1952790.732009-01-012012-01-012017-01-012017-01-01BachelorBachelorITEngineering{'Business Transformation': 16.0, 'Program Management': 15.0, 'Risk Management': 12.0, 'Project Management': 13.0, 'Performance Management': 8.0, 'Business Process Improvement': 12.0}12.48Junior
1069Greater Sydney AreaPhillip ZhangKPMG AustraliaSenior ConsultantNaNJan 2024 - Present2024-01-016 mosKPMG AustraliaConsultantFeb 2022 - Jan 20242022-02-01UNSWhttps://www.linkedin.com/company/6096/2017 - 2021NaNNaN2011 - 2016Leadership, Teamwork, Critical Thinking, Agile Project Management, Financial Analysis, Client Engagement, Microsoft Office, Microsoft Excel, Microsoft PowerPoint, Microsoft Word, Communication, Time Management, Decision-Making, Customer Service, Agile Leadership, Interpersonal Skills, Public Speaking, Wellness, PresentationsLeadership9.0Teamwork10.0Critical Thinking5.0Agile Project ManagementNaNFinancial Analysis1.0Client Engagement1.0NaN2024-01-012024-06-24 12:46:00.1953080.482017-01-012021-01-012011-01-012016-01-01DiplomaUnknownITUnknown{'Leadership': 9.0, 'Teamwork': 10.0, 'Critical Thinking': 5.0, 'Financial Analysis': 1.0, 'Client Engagement': 1.0}3.48Junior
1070San Francisco Bay AreaStephanie ZhangKPMG USConsultantNaNOct 2022 - Present2022-10-011 yr 9 mosKPMG USSenior Associate AdvisoryOct 2018 - Oct 20222018-10-01Ichec Brussels Management Schoolhttps://www.linkedin.com/company/15092365/NaNhttps://www.linkedin.com/company/3084/Bachelor of Science (B.S.), Accounting and Business Administration | Minor: Enterprise Information SystemsNaNAccounting, Teamwork, Time Management, Financial Accounting, Events, Event Planning, Invoicing, Access, Microsoft Office, Microsoft Excel, Microsoft Word, PowerPoint, SAP FICO, SAP ERP, SAP MM, Microsoft Dynamics, Visio, Financial Analysis, Bookkeeping, Financial Reporting, Social Media, Research, Marketing, Salesforce.com, PhotoshopAccounting11.0Teamwork12.0Time Management13.0Financial Accounting6.0Events4.0Event Planning7.0NaN2022-10-012024-06-24 12:46:00.1953231.73NaTNaTNaTNaTBachelorBachelorITIT{'Accounting': 11.0, 'Teamwork': 12.0, 'Time Management': 13.0, 'Financial Accounting': 6.0, 'Events': 4.0, 'Event Planning': 7.0}NaNMid-level
1071Sydney, New South Wales, AustraliaElian ZhengKPMG AustraliaSenior ConsultantNaNJul 2022 - Present2022-07-012 yrsKPMG AustraliaConsultant | Data & CloudFeb 2021 - Jul 20222021-02-01UNSWhttps://www.linkedin.com/company/6096/2015 - 2020NaNNaN2009 - 2014Teamwork, Leadership, Communication, Microsoft Office, Management, Customer Service, Research, C, Critical Thinking, Presentation Skills, Problem Solving, Data Analysis, Microsoft Excel, Office 365, SpreadsheetsTeamwork10.0Leadership9.0Communication9.0Microsoft Office3.0Management4.0Customer Service2.0NaN2022-07-012024-06-24 12:46:00.1953361.982015-01-012020-01-012009-01-012014-01-01High SchoolUnknownHealthUnknown{'Teamwork': 10.0, 'Leadership': 9.0, 'Communication': 9.0, 'Microsoft Office': 3.0, 'Management': 4.0, 'Customer Service': 2.0}4.48Mid-level
1075Jing'an District, Shanghai, ChinaCathy ZhouKPMG ChinaSenior ConsultantShanghai City, ChinaFeb 2017 - Present2017-02-017 yrs 5 mosKPMG USDirectorJun 2005 - Jan 20172005-06-01Baruch Collegehttps://www.linkedin.com/company/4365/NaNhttps://www.linkedin.com/company/91038/Bachelor's Degree, AccountingNaNAML, OFAC, Economic Sanctions, FCPA, Tax, Financial Services, Due Diligence, Risk ManagementAML1.0OFAC3.0Economic Sanctions1.0FCPA2.0TaxNaNFinancial ServicesNaNNaN2017-02-012024-06-24 12:46:00.1953517.39NaTNaTNaTNaTDiplomaDiplomaHealthHealth{'AML': 1.0, 'OFAC': 3.0, 'Economic Sanctions': 1.0, 'FCPA': 2.0}NaNSenior
1076Xuhui District, Shanghai, ChinaIrene ZhouKPMG ChinaConsultantShanghaiOct 2012 - Present2012-10-0111 yrs 9 mosHSBC Commercial BankingCredit Risk Management internSep 2011 - Dec 20112011-09-01Fudan Universityhttps://www.linkedin.com/company/22859/2008 - 2012NaNNaN2005 - 2008CICPA, Finance, Auditing, Internal Controls, IFRS, Analysis, Sarbanes-Oxley Act, Accounting, Management, GAAP, Financial AnalysisCICPANaNFinanceNaNAuditingNaNInternal ControlsNaNIFRSNaNAnalysisNaNNaN2012-10-012024-06-24 12:46:00.19536711.732008-01-012012-01-012005-01-012008-01-01BachelorUnknownEngineeringUnknown{}12.48Senior
1077Sydney, New South Wales, AustraliaRyan ZhuKPMG AustraliaManagerAustraliaMay 2022 - Present2022-05-012 yrs 2 mosDoorDashSoftware EngineerNov 2021 - May 20222021-11-01University of Sydneyhttps://www.linkedin.com/company/166676/2006 - 2007https://www.linkedin.com/company/9727/Master of Science - MS, Information and Communication Technology2005 - 2006MobX, GatsbyJS, Adobe Experience Manager (AEM), React.js, Java, Business Analysis, Project Management, Node.js, Webpack, Spring, Amazon Web Services (AWS), JavaScript, AngularJS, jQuery, Microsoft SQL Server, MySQL, CSS, HTML, SOAP, XML, J2EE Application Development, Axis2, Technical Data Analysis, TypeScript, Amazon Dynamodb, Serverless, Bootstrap, GraphQLMobX1.0GatsbyJS3.0Adobe Experience Manager (AEM)3.0React.js9.0Java8.0Business Analysis5.0NaN2022-05-012024-06-24 12:46:00.1953822.152006-01-012007-01-012005-01-012006-01-01DiplomaDiplomaITHealth{'MobX': 1.0, 'GatsbyJS': 3.0, 'Adobe Experience Manager (AEM)': 3.0, 'React.js': 9.0, 'Java': 8.0, 'Business Analysis': 5.0}17.48Mid-level